GluN2B mutant rescue in grin2b knockout: EPSC properties

This document forms part of the data and code deposited at:
https://github.com/acp29/Elmasri_GRIN2B

Load package requirements

if (!require(package="tidyverse")) utils::install.packages("tidyverse")
library(tidyverse) 
if (!require(package="lme4")) utils::install.packages("lme4")
library(lme4)  
if (!require(package="HLMdiag")) utils::install.packages("HLMdiag")
library(HLMdiag) 
if (!require(package="parameters")) utils::install.packages("parameters")
library(parameters) 
if (!require(package="car")) utils::install.packages("car")
library(car)  
if (!require(package="performance")) utils::install.packages("performance")
library(performance)  
if (!require(package="BayesFactor")) utils::install.packages("BayesFactor")
library(BayesFactor) 
if (!require(package="bayestestR")) utils::install.packages("bayestestR")
library(bayestestR) 
if (!require(package="stats")) utils::install.packages("stats")
library(stats)
if (!require(package="pCalibrate")) utils::install.packages("pCalibrate")
library(pCalibrate)
if (!require(package="afex")) utils::install.packages("afex")
library(afex)
if (!require(package="emmeans")) utils::install.packages("emmeans")
library(emmeans)
if (!require(package="multcomp")) utils::install.packages("multcomp")
library(multcomp)
if (!require(package="knitr")) utils::install.packages("knitr")
library(knitr)
if (!require(package="kableExtra")) utils::install.packages("kableExtra")
library(kableExtra)
if (!require(package="ggplot2")) utils::install.packages("ggplot2")
library(ggplot2)
if (!require(package="qqplotr")) utils::install.packages("qqplotr")
library(qqplotr)
if (!require(package="gridExtra")) utils::install.packages("gridExtra")
library(gridExtra)
if (!require(package="ggforce")) utils::install.packages("ggforce")
library(ggforce)
if (!require(package="devEMF")) utils::install.packages("devEMF")
library(devEMF)
if (!require(package="effectsize")) utils::install.packages("effectsize")
library(effectsize)

Read text in from file

Data <- read.delim("../data/n2b_mutant.dat", header = TRUE)
# Implicit nesting (required for anovaBF)
Data %>% 
  mutate(mutation = as.factor(mutation)) %>% 
  mutate(animal = paste0(as.numeric(mutation),animal)) %>% 
  mutate(slice = paste0(animal,slice)) %>% 
  mutate(pair = paste0(slice,pair)) %>% 
  mutate(pair = factor(pair)) -> Data

Factor encoding

Data$mutation <- as.factor(Data$mutation)
Data$transfection <- as.factor(Data$transfection)
Data$animal <- as.factor(Data$animal)
Data$slice <- as.factor(Data$slice)
Data$pair <- as.factor(Data$pair)

Set mutation WT and transfection - as reference levels

Data$mutation <- factor(Data$mutation, levels=c("WT","R540H","R696H","C456Y","C461F"))
Data$transfection <- factor(Data$transfection, levels=c("-","+"))

lmer settings

settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore",  tol = 1e-4), boundary.tol=0)

Fit a mixed linear model

# Initialize
variates <- c("peaknmda","decaynmda","dt50nmda","chargenmda","peakampa","decayampa","dt50ampa","chargeampa")
l <- length(variates)

for (i in 1:l) {
  
variates[i] -> resp  

cat('\n\n\n# Analysis of',resp,'\n\n')

# Plot data
# colours selected from:
#  > library(scales)
#  > show_col(hue_pal()(9))
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.2, color="grey") +
    geom_point(aes(mutation_jittered), alpha=0.4, shape=16) +
    scale_color_manual(values=c("grey","#00BA38")) +
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0,inherit.aes=FALSE) + 
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1, inherit.aes=FALSE) +
    ylab(resp) +
    ggtitle("a") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = "top")
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.9, shape = 16) + 
    scale_color_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    stat_summary(fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0) + 
    stat_summary(fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1) +
    ylab("ratio") +
    ggtitle("b") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none")
grid.arrange(p1, p2, nrow = 1, ncol = 2, top=sprintf("Summary plots of the data for: %s\n",resp))

# Fit the model with planned contrasts and perform hypothesis testing
  
# Setup planned, orthogonal contrasts
# ("WT","R540H","R696H","C456Y","C461F")
# According to features of the mutations or experiments in heterologous expression systems
WT_vs_Mutants <- c(-4,1,1,1,1)/5
LOF_vs_GOF    <- c(0,2,2,-2,-2)/4
LOF           <- c(0,0,0,-1,1)/2
GOF           <- c(0,-1,1,0,0)/2
contr.orth <- cbind(WT_vs_Mutants, LOF_vs_GOF, LOF, GOF)
rownames(contr.orth) <- levels(Data$mutation)

# Check that contrasts are indeed orthogonal
contr.orth %>%
  cor() %>%
  knitr::kable(caption = sprintf("**All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contr.orth %>%
  colSums() %>%
  as.data.frame() %>%
  rename(.,'sum' = '.') %>%
  knitr::kable(caption = sprintf("**Sum of each orthogonal contrast should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Fit model (with orthogonal contrasts)
contrasts(Data$mutation) <- contr.orth
attr(Data$mutation,"contrasts") %>%
  as.data.frame() %>%
  rownames_to_column(var = "mutation") %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contrasts(Data$transfection) <- rbind(-1,1)/2
attr(Data$transfection,"contrasts") %>% 
  as.data.frame() %>%
  rename(contrast = "V1") %>%
  rownames_to_column(var = "transfection") %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on transfection: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print() 
formula <- sprintf("log(%s) ~ mutation * transfection + (1|animal/slice/pair)", resp)
model <- lme4::lmer(formula, data = Data, REML = TRUE, control = settings, na.action = "na.fail") 

# Checking model assumptions
resid = residuals(model)
n = length(resid)
stdev = sqrt((n-1)/n) * sd(resid) # standard deviation with denominator n
std_resid = resid/stdev
p1 <- ggplot(Data, aes(x = fitted(model), y = std_resid)) +
  geom_point() +
  ggtitle("a") +
  xlab("Fitted values") + ylab("Standardized Residuals") +
  geom_hline(yintercept = 0) +
  geom_quantile(formula=y~x, color="#619CFF", size=1) +
  geom_smooth(method="loess", formula = y ~ x, color="#F8766D", size=1, se=FALSE)
p2 <- ggplot(Data, aes(x = std_resid)) +
  geom_histogram(aes(y=..density..), binwidth = 0.9*n^(-1/5), fill="#619CFF", alpha=0.33)  +
  geom_density(kernel="gaussian", alpha=0, color="#619CFF", size=1) +
  ggtitle("b") +
  xlab("Standardized Residuals") + ylab("Density") +
  geom_vline(xintercept = 0) +
  geom_function(fun = dnorm, args = list(mean=0, sd=1), col = "#F8766D", size = 1)
p3 <- ggplot(Data, aes(sample = std_resid)) +
  geom_qq_band(distribution = "norm", bandType = "ts", mapping = aes(fill = "TS"), fill="#619CFF", alpha = 0.33) +
  stat_qq() + 
  stat_qq_line(color="#F8766D",size=1) +
  ggtitle("c") +
  xlab("Normal Quantiles") + ylab("Sample Quantiles") 
infl <- hlm_influence(model, level="pair:(slice:animal)")
p4 <- infl %>% 
  mutate(influential = cooksd > 1.0) %>% 
  ggplot(aes(x=`pair:(slice:animal)`,y=cooksd, color=influential)) + 
  geom_segment(aes(x=`pair:(slice:animal)`, xend=`pair:(slice:animal)`, y=0, yend=cooksd)) + 
  geom_point() + 
  ylab("Cook's distance") +
  scale_color_manual(values=c("#619CFF","#F8766D")) + 
  ggtitle("d") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        panel.background = element_rect(color="#EBEBEB"),
        panel.grid = element_blank(),
        panel.grid.minor.y = element_line(color = "white", size=0.25),
        panel.grid.major.y = element_line(color = "white", size=0.5),
        axis.line = element_blank(),
        axis.line.x = element_line(size = 0.5, colour = "black"))
grid.arrange(p1, p2, p3, p4, nrow=2, ncol=2, top=sprintf("Plots of standardized model residuals and Cook's distances: %s\n",resp))

# Calculate ANOVA table for the fitted model (Type III sum of squares) 
car::Anova(model, type = 3, test.statistic = "F") %>%       # Uses Kenward-Roger degrees of freedom
  as.data.frame() %>%
  rownames_to_column(var="Source") %>%
  filter(Source != "(Intercept)") -> aov

# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
Data %>% 
  mutate(logresp = log(!!sym(resp))) %>%
  as.data.frame() -> Data
set.seed(123456)
anovaBF(logresp ~ mutation * transfection + animal + slice + pair, 
                 whichRandom = c("animal","slice","pair"), 
                 whichModels = "withmain", 
                 iterations = 20000,
                 data = Data) %>%
  bayesfactor_inclusion(match_models = TRUE) %>% 
  as.data.frame() %>% 
  na.omit() %>%    # removes the (nuisance) random factors
  mutate(BF = exp(log_BF)) %>%
  mutate_at("BF", formatC, format='g',digits = 3) %>% 
  dplyr::select(BF) %>% 
  unlist() -> aov$BF

# Calculate orthogonal contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model) 
# to an F statistic but they give identical p-values; I think this makes more sense and provides 
# more consistency when splitting the source of variation into orthogonal contrasts and presenting 
# them in an ANOVA table (eg. like with summary.aov or orthogonal contrasts in SAS)
model_parameters(model, ci_method = "kenward", exponentiate = TRUE, effects = "fixed") %>%  
  filter(grepl(":",Parameter)) %>%                          # select interaction terms only
  rename(Source = Parameter) %>%                            # set denominator degrees of freedom
  mutate(Df = 1) %>%                                        # set numerator degrees of freedom
  add_column(BF = "")  %>%                                  # add empty column for Bayes factors
  rename(Df.res = df_error) %>%                             # set denominator degrees of freedom
  mutate(F = abs(t)^2) %>%                                  # calculate F statistic
  mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>%   # calculate p value
  dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>%      # select columns of interest for table
  rbind(aov,.) %>%                                          # row bind with anova table
  mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>%       # format p values as APA style
  knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  add_indent(c(4:7)) %>%                                    # add indentation to indicate source components
  print()

# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>% 
  as.data.frame() %>% 
  mutate(N = ngrps(model)) %>%
  rbind(.,c("residual",1-sum(.$ICC),nobs(model))) %>%
  mutate(ICC = as.numeric(ICC)) %>%                               
  knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * transfection, data = Data, tran = 'log', type = 'response')
emm %>% 
  summary(calc = c(n = ".wgt.")) %>%
  as.data.frame() %>%
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  relocate(df, .before = response) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculate overall average for untransfected neurons
emmeans(model, ~ mutation * transfection, data = Data) %>%
  as.data.frame() %>%
  filter(transfection == "-") %>%
  dplyr::select(emmean) %>% 
  colMeans() %>%
  exp() %>%
  sprintf("**Overall average of %s for untransfected neurons**: %.2f",resp,.) %>%
  print()

# Calculate transfected/untransfected ratios
emm.transfection <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.transfection %>%
  confint() %>%
  as.data.frame() %>%
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for transfected/untransfected ratios: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# 95% confidence intervals for interaction contrasts
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
  confint() %>% 
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>% 
    as.data.frame() %>% 
    mutate(n = df+nrow(.)+1) %>% 
    mutate(r = t_to_r(t.ratio, df)$r) %>% 
    mutate(z = atanh(r),
           SE = 1/sqrt(n-3),
           CI = sprintf("[%.2f, %.2f]",
                        LL = tanh(z - 1.96*SE),
                        UL = tanh(z + 1.96*SE))) %>%
    dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
    knitr::kable(col.names = c("mutation",
                               "transfection",
                               "*n*",
                               "*r*",
                               "95% *CI*"),
                 caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    print()

posthoc = TRUE

if (posthoc == TRUE) {
  
# p-values and maximum Bayes Factors for interaction contrasts 
# Dunnett's step-down adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction %>%   
    as.glht() %>%
    summary(test = adjusted(type = "free")) -> glht.out  
emm.interaction %>%   
  as.data.frame() %>%
  dplyr::select(-SE) %>%
  mutate(p.adj = glht.out$test$pvalues) %>%
  mutate(p.adj = sapply(p.adj,max,.Machine$double.eps)) %>%
  mutate(maxBF = 1/pCalibrate(p.adj,"exploratory")) %>%
  mutate_at("maxBF", formatC, format='g',digits = 3) %>%
  mutate(p.value = afex::round_ps_apa(p.value)) %>%
  mutate(p.adj = afex::round_ps_apa(p.adj)) %>%
  knitr::kable(caption = sprintf("**Hypothesis testing on interaction parameters (Dunnett's step-down p-value adjustment): %s**",resp), digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

}
# Replot data with 95% confidence intervals
emf(sprintf("../img/%s_%s.emf","n2b_mutants",resp), width=4.5, height=3.5)
emm %>%
    as.data.frame() %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.3, color="grey", size=0.75) +
    geom_point(aes(mutation_jittered), alpha=0.6, shape = 16, size=1.25) +
    scale_color_manual(values=c("grey","#00BA38")) +
    scale_fill_manual(values=c("grey","#00BA38")) +
    geom_crossbar(data = emm_df, 
                    aes(x=mutation_transfection, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=transfection), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.3, inherit.aes=FALSE) + 
    ylab(resp) +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = c(0.5, 1.06),
          legend.direction = "horizontal",
          text = element_text(size=14))
emm.transfection %>%
    confint() %>%
    as.data.frame() -> emm.transfection_df
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) + 
    geom_crossbar(data = emm.transfection_df, 
                           aes(x=mutation, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=mutation), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
    scale_colour_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    scale_fill_manual(values=c("grey","#DB72FB","#FF61C3","#619CFF","#00C19F")) +
    ylab("ratio") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1), axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none",
          text=element_text(size=14))
grid.arrange(p1, p2, layout_matrix=rbind(c(1,2)), top=sprintf("Summary plots of the data with 95%% confidence intervals: %s\n",resp))
dev.off() #turn off device and finalize file


}

Analysis of peaknmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peaknmda

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: peaknmda

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: peaknmda

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: peaknmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peaknmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	2.91	4	16.16	.055	2.02
transfection	51.00	1	91.00	<.001	1.22e+08
mutation:transfection	0.18	4	91.00	.947	0.0511
mutationWT_vs_Mutants:transfection1	0.43	1	91.00	.513
mutationLOF_vs_GOF:transfection1	0.28	1	91.00	.595
mutationLOF:transfection1	0.07	1	91.00	.796
mutationGOF:transfection1	0.00	1	91.00	.960

Intraclass correlation coefficients for random effects: peaknmda

Group	ICC	N
pair:(slice:animal)	0.147	96
slice:animal	0.311	62
animal	0.335	22
residual	0.207	192

Estimated marginal means with 95% confidence intervals: peaknmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	16.54	162.54	19	86.74	304.60
R540H	-	17.48	92.40	27	54.27	157.31
R696H	-	18.13	43.36	17	22.88	82.17
C456Y	-	20.22	108.96	18	60.57	196.00
C461F	-	16.32	71.66	15	34.80	147.57
WT	+	16.54	118.04	19	62.99	221.21
R540H	+	17.48	63.69	27	37.41	108.44
R696H	+	18.13	29.65	17	15.64	56.18
C456Y	+	20.22	71.74	18	39.88	129.05
C461F	+	16.32	44.99	15	21.85	92.64

[1] “Overall average of peaknmda for untransfected neurons: 87.35”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peaknmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	0.73	0.57	0.92
(+) / (-)	R540H	91	0.69	0.56	0.84
(+) / (-)	R696H	91	0.68	0.53	0.88
(+) / (-)	C456Y	91	0.66	0.51	0.84
(+) / (-)	C461F	91	0.63	0.48	0.82

95% confidence intervals for contrasts: peaknmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.95	0.69	1.30
R696H / WT	(+) / (-)	91	0.94	0.66	1.33
C456Y / WT	(+) / (-)	91	0.91	0.64	1.28
C461F / WT	(+) / (-)	91	0.86	0.60	1.24

Standardized effect sizes (r) for contrasts: peaknmda

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.03	[-0.23, 0.17]
R696H / WT	(+) / (-)	96	-0.04	[-0.23, 0.17]
C456Y / WT	(+) / (-)	96	-0.06	[-0.26, 0.14]
C461F / WT	(+) / (-)	96	-0.08	[-0.28, 0.12]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): peaknmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.95	91	1	-0.33	.742	.916	1
R696H / WT	(+) / (-)	0.94	91	1	-0.34	.732	.916	1
C456Y / WT	(+) / (-)	0.91	91	1	-0.57	.573	.894	1
C461F / WT	(+) / (-)	0.86	91	1	-0.80	.425	.840	1

Analysis of decaynmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decaynmda

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: decaynmda

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: decaynmda

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: decaynmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decaynmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	2.96	4	15.57	.054	0.638
transfection	48.58	1	91.00	<.001	1.16e+07
mutation:transfection	8.59	4	91.00	<.001	1.08e+05
mutationWT_vs_Mutants:transfection1	19.12	1	91.00	<.001
mutationLOF_vs_GOF:transfection1	16.39	1	91.00	<.001
mutationLOF:transfection1	0.74	1	91.00	.390
mutationGOF:transfection1	0.15	1	91.00	.700

Intraclass correlation coefficients for random effects: decaynmda

Group	ICC	N
pair:(slice:animal)	0.000	96
slice:animal	0.010	62
animal	0.149	22
residual	0.842	192

Estimated marginal means with 95% confidence intervals: decaynmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	26.53	71.37	19	54.92	92.76
R540H	-	26.94	71.65	27	57.08	89.93
R696H	-	30.04	56.15	17	42.83	73.61
C456Y	-	35.56	70.96	18	55.12	91.34
C461F	-	25.11	99.19	15	73.66	133.57
WT	+	26.53	78.86	19	60.68	102.49
R540H	+	26.94	56.46	27	44.99	70.87
R696H	+	30.04	41.33	17	31.52	54.19
C456Y	+	35.56	34.36	18	26.69	44.23
C461F	+	25.11	40.44	15	30.04	54.46

[1] “Overall average of decaynmda for untransfected neurons: 72.63”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decaynmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	1.10	0.85	1.43
(+) / (-)	R540H	91	0.79	0.63	0.98
(+) / (-)	R696H	91	0.74	0.56	0.97
(+) / (-)	C456Y	91	0.48	0.37	0.63
(+) / (-)	C461F	91	0.41	0.30	0.55

95% confidence intervals for contrasts: decaynmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.71	0.51	1.00
R696H / WT	(+) / (-)	91	0.67	0.46	0.97
C456Y / WT	(+) / (-)	91	0.44	0.30	0.64
C461F / WT	(+) / (-)	91	0.37	0.25	0.55

Standardized effect sizes (r) for contrasts: decaynmda

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.20	[-0.39, -0.00]
R696H / WT	(+) / (-)	96	-0.22	[-0.40, -0.02]
C456Y / WT	(+) / (-)	96	-0.42	[-0.57, -0.24]
C461F / WT	(+) / (-)	96	-0.47	[-0.61, -0.30]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): decaynmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.71	91	1	-1.98	.051	.064	2.08
R696H / WT	(+) / (-)	0.67	91	1	-2.13	.035	.064	2.08
C456Y / WT	(+) / (-)	0.44	91	1	-4.40	<.001	<.001	363
C461F / WT	(+) / (-)	0.37	91	1	-5.06	<.001	<.001	3.19e+03

Analysis of dt50nmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50nmda

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: dt50nmda

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: dt50nmda

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: dt50nmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50nmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	4.86	4	15.99	.009	3.59
transfection	57.81	1	91.00	<.001	4.09e+07
mutation:transfection	10.06	4	91.00	<.001	6.05e+05
mutationWT_vs_Mutants:transfection1	21.45	1	91.00	<.001
mutationLOF_vs_GOF:transfection1	18.48	1	91.00	<.001
mutationLOF:transfection1	0.87	1	91.00	.352
mutationGOF:transfection1	1.27	1	91.00	.263

Intraclass correlation coefficients for random effects: dt50nmda

Group	ICC	N
pair:(slice:animal)	0.000	96
slice:animal	0.043	62
animal	0.224	22
residual	0.733	192

Estimated marginal means with 95% confidence intervals: dt50nmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	22.91	34.63	19	27.92	42.94
R540H	-	24.51	32.52	27	27.04	39.11
R696H	-	25.55	23.50	17	18.85	29.30
C456Y	-	29.43	35.03	18	28.57	42.95
C461F	-	21.89	35.99	15	28.16	46.00
WT	+	22.91	36.94	19	29.79	45.80
R540H	+	24.51	28.26	27	23.49	33.99
R696H	+	25.55	17.78	17	14.26	22.17
C456Y	+	29.43	20.34	18	16.59	24.94
C461F	+	21.89	18.35	15	14.36	23.46

[1] “Overall average of dt50nmda for untransfected neurons: 31.96”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50nmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	1.07	0.89	1.28
(+) / (-)	R540H	91	0.87	0.75	1.01
(+) / (-)	R696H	91	0.76	0.62	0.92
(+) / (-)	C456Y	91	0.58	0.48	0.70
(+) / (-)	C461F	91	0.51	0.42	0.63

95% confidence intervals for contrasts: dt50nmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.81	0.64	1.03
R696H / WT	(+) / (-)	91	0.71	0.54	0.92
C456Y / WT	(+) / (-)	91	0.54	0.42	0.71
C461F / WT	(+) / (-)	91	0.48	0.36	0.63

Standardized effect sizes (r) for contrasts: dt50nmda

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.18	[-0.37, 0.02]
R696H / WT	(+) / (-)	96	-0.26	[-0.44, -0.06]
C456Y / WT	(+) / (-)	96	-0.44	[-0.59, -0.26]
C461F / WT	(+) / (-)	96	-0.49	[-0.63, -0.32]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): dt50nmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.81	91	1	-1.72	.088	.088	1.72
R696H / WT	(+) / (-)	0.71	91	1	-2.59	.011	.021	4.54
C456Y / WT	(+) / (-)	0.54	91	1	-4.65	<.001	<.001	783
C461F / WT	(+) / (-)	0.48	91	1	-5.38	<.001	<.001	1.07e+04

Analysis of chargenmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargenmda

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: chargenmda

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: chargenmda

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: chargenmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargenmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	3.28	4	15.86	.039	2.16
transfection	43.33	1	91.00	<.001	8.24e+06
mutation:transfection	2.26	4	91.00	.069	0.856
mutationWT_vs_Mutants:transfection1	3.97	1	91.00	.049
mutationLOF_vs_GOF:transfection1	4.57	1	91.00	.035
mutationLOF:transfection1	0.50	1	91.00	.482
mutationGOF:transfection1	0.96	1	91.00	.329

Intraclass correlation coefficients for random effects: chargenmda

Group	ICC	N
pair:(slice:animal)	0.112	96
slice:animal	0.249	62
animal	0.224	22
residual	0.414	192

Estimated marginal means with 95% confidence intervals: chargenmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	18.28	16.39	19	8.18	32.83
R540H	-	18.86	8.67	27	4.79	15.71
R696H	-	20.64	3.51	17	1.72	7.15
C456Y	-	23.75	11.18	18	5.79	21.62
C461F	-	17.90	8.73	15	3.94	19.36
WT	+	18.28	12.74	19	6.36	25.52
R540H	+	18.86	4.61	27	2.54	8.34
R696H	+	20.64	2.46	17	1.21	5.01
C456Y	+	23.75	4.84	18	2.50	9.35
C461F	+	17.90	3.01	15	1.36	6.68

[1] “Overall average of chargenmda for untransfected neurons: 8.66”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargenmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	0.78	0.51	1.18
(+) / (-)	R540H	91	0.53	0.37	0.75
(+) / (-)	R696H	91	0.70	0.45	1.09
(+) / (-)	C456Y	91	0.43	0.28	0.66
(+) / (-)	C461F	91	0.35	0.22	0.55

95% confidence intervals for contrasts: chargenmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.68	0.40	1.18
R696H / WT	(+) / (-)	91	0.90	0.49	1.66
C456Y / WT	(+) / (-)	91	0.56	0.31	1.01
C461F / WT	(+) / (-)	91	0.44	0.24	0.83

Standardized effect sizes (r) for contrasts: chargenmda

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.14	[-0.33, 0.06]
R696H / WT	(+) / (-)	96	-0.04	[-0.23, 0.17]
C456Y / WT	(+) / (-)	96	-0.20	[-0.38, 0.00]
C461F / WT	(+) / (-)	96	-0.26	[-0.44, -0.06]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): chargenmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.68	91	1	-1.39	.169	.283	1.03
R696H / WT	(+) / (-)	0.90	91	1	-0.33	.739	.739	1
C456Y / WT	(+) / (-)	0.56	91	1	-1.94	.055	.135	1.36
C461F / WT	(+) / (-)	0.44	91	1	-2.57	.012	.041	2.8

Analysis of peakampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peakampa

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: peakampa

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: peakampa

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: peakampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peakampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	9.18	4	16.25	<.001	62
transfection	0.72	1	91.00	.399	0.186
mutation:transfection	1.20	4	91.00	.315	0.204
mutationWT_vs_Mutants:transfection1	0.19	1	91.00	.662
mutationLOF_vs_GOF:transfection1	1.32	1	91.00	.253
mutationLOF:transfection1	3.44	1	91.00	.067
mutationGOF:transfection1	0.10	1	91.00	.747

Intraclass correlation coefficients for random effects: peakampa

Group	ICC	N
pair:(slice:animal)	0.085	96
slice:animal	0.184	62
animal	0.339	22
residual	0.392	192

Estimated marginal means with 95% confidence intervals: peakampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	18.13	5.72	19	3.18	10.28
R540H	-	19.52	18.12	27	11.02	29.79
R696H	-	19.82	51.30	17	28.24	93.18
C456Y	-	22.12	20.57	18	11.90	35.56
C461F	-	17.72	20.77	15	10.59	40.72
WT	+	18.13	5.05	19	2.81	9.08
R540H	+	19.52	18.35	27	11.17	30.17
R696H	+	19.82	55.69	17	30.66	101.15
C456Y	+	22.12	22.42	18	12.97	38.75
C461F	+	17.72	14.46	15	7.37	28.34

[1] “Overall average of peakampa for untransfected neurons: 18.67”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peakampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	0.88	0.65	1.21
(+) / (-)	R540H	91	1.01	0.78	1.32
(+) / (-)	R696H	91	1.09	0.78	1.51
(+) / (-)	C456Y	91	1.09	0.79	1.51
(+) / (-)	C461F	91	0.70	0.49	0.99

95% confidence intervals for contrasts: peakampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	1.15	0.76	1.73
R696H / WT	(+) / (-)	91	1.23	0.78	1.94
C456Y / WT	(+) / (-)	91	1.23	0.78	1.94
C461F / WT	(+) / (-)	91	0.79	0.49	1.27

Standardized effect sizes (r) for contrasts: peakampa

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	0.07	[-0.13, 0.27]
R696H / WT	(+) / (-)	96	0.09	[-0.11, 0.29]
C456Y / WT	(+) / (-)	96	0.10	[-0.11, 0.29]
C461F / WT	(+) / (-)	96	-0.10	[-0.30, 0.10]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): peakampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	1.15	91	1	0.66	.512	.712	1
R696H / WT	(+) / (-)	1.23	91	1	0.89	.375	.712	1
C456Y / WT	(+) / (-)	1.23	91	1	0.92	.360	.712	1
C461F / WT	(+) / (-)	0.79	91	1	-1.00	.320	.712	1

Analysis of decayampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decayampa

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: decayampa

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: decayampa

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: decayampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decayampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	2.41	4	16.5	.091	1.46
transfection	1.54	1	91.0	.217	0.408
mutation:transfection	0.95	4	91.0	.441	0.159
mutationWT_vs_Mutants:transfection1	0.01	1	91.0	.938
mutationLOF_vs_GOF:transfection1	0.00	1	91.0	.952
mutationLOF:transfection1	3.76	1	91.0	.056
mutationGOF:transfection1	0.00	1	91.0	.949

Intraclass correlation coefficients for random effects: decayampa

Group	ICC	N
pair:(slice:animal)	0.000	96
slice:animal	0.261	62
animal	0.454	22
residual	0.285	192

Estimated marginal means with 95% confidence intervals: decayampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	17.41	8.34	19	5.12	13.61
R540H	-	18.59	6.89	27	4.57	10.37
R696H	-	18.56	3.44	17	2.10	5.64
C456Y	-	20.25	7.55	18	4.80	11.85
C461F	-	17.24	4.57	15	2.60	8.02
WT	+	17.41	7.93	19	4.86	12.93
R540H	+	18.59	6.44	27	4.28	9.69
R696H	+	18.56	3.24	17	1.98	5.32
C456Y	+	20.25	6.13	18	3.90	9.62
C461F	+	17.24	5.03	15	2.86	8.83

[1] “Overall average of decayampa for untransfected neurons: 5.84”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decayampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	0.95	0.77	1.17
(+) / (-)	R540H	91	0.93	0.79	1.11
(+) / (-)	R696H	91	0.94	0.76	1.17
(+) / (-)	C456Y	91	0.81	0.66	1.00
(+) / (-)	C461F	91	1.10	0.87	1.38

95% confidence intervals for contrasts: decayampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.98	0.75	1.28
R696H / WT	(+) / (-)	91	0.99	0.74	1.34
C456Y / WT	(+) / (-)	91	0.85	0.64	1.14
C461F / WT	(+) / (-)	91	1.16	0.85	1.57

Standardized effect sizes (r) for contrasts: decayampa

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.01	[-0.21, 0.19]
R696H / WT	(+) / (-)	96	-0.01	[-0.21, 0.20]
C456Y / WT	(+) / (-)	96	-0.11	[-0.31, 0.09]
C461F / WT	(+) / (-)	96	0.10	[-0.10, 0.29]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): decayampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.98	91	1	-0.12	.902	.989	1
R696H / WT	(+) / (-)	0.99	91	1	-0.05	.959	.989	1
C456Y / WT	(+) / (-)	0.85	91	1	-1.07	.288	.664	1
C461F / WT	(+) / (-)	1.16	91	1	0.94	.348	.666	1

Analysis of dt50ampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50ampa

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: dt50ampa

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: dt50ampa

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: dt50ampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50ampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	3.20	4	15.93	.042	1.67
transfection	3.05	1	91.00	.084	0.704
mutation:transfection	0.31	4	91.00	.872	0.065
mutationWT_vs_Mutants:transfection1	0.89	1	91.00	.349
mutationLOF_vs_GOF:transfection1	0.04	1	91.00	.839
mutationLOF:transfection1	0.04	1	91.00	.838
mutationGOF:transfection1	0.30	1	91.00	.588

Intraclass correlation coefficients for random effects: dt50ampa

Group	ICC	N
pair:(slice:animal)	0.125	96
slice:animal	0.038	62
animal	0.237	22
residual	0.599	192

Estimated marginal means with 95% confidence intervals: dt50ampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	20.82	4.29	19	3.25	5.66
R540H	-	22.29	3.78	27	2.98	4.79
R696H	-	23.14	2.60	17	1.96	3.45
C456Y	-	26.51	3.96	18	3.05	5.14
C461F	-	19.89	3.12	15	2.28	4.28
WT	+	20.82	4.31	19	3.27	5.69
R540H	+	22.29	3.57	27	2.81	4.53
R696H	+	23.14	2.28	17	1.71	3.02
C456Y	+	26.51	3.47	18	2.67	4.51
C461F	+	19.89	2.83	15	2.06	3.88

[1] “Overall average of dt50ampa for untransfected neurons: 3.49”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50ampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	1.01	0.82	1.24
(+) / (-)	R540H	91	0.95	0.80	1.13
(+) / (-)	R696H	91	0.88	0.70	1.09
(+) / (-)	C456Y	91	0.88	0.71	1.08
(+) / (-)	C461F	91	0.91	0.72	1.14

95% confidence intervals for contrasts: dt50ampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.94	0.72	1.23
R696H / WT	(+) / (-)	91	0.87	0.65	1.18
C456Y / WT	(+) / (-)	91	0.87	0.65	1.17
C461F / WT	(+) / (-)	91	0.90	0.66	1.23

Standardized effect sizes (r) for contrasts: dt50ampa

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.05	[-0.25, 0.15]
R696H / WT	(+) / (-)	96	-0.09	[-0.29, 0.11]
C456Y / WT	(+) / (-)	96	-0.10	[-0.29, 0.11]
C461F / WT	(+) / (-)	96	-0.07	[-0.27, 0.13]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): dt50ampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.94	91	1	-0.45	.654	.767	1
R696H / WT	(+) / (-)	0.87	91	1	-0.91	.366	.767	1
C456Y / WT	(+) / (-)	0.87	91	1	-0.92	.360	.767	1
C461F / WT	(+) / (-)	0.90	91	1	-0.67	.506	.767	1

Analysis of chargeampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargeampa

	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT_vs_Mutants	1	0	0	0
LOF_vs_GOF	0	1	0	0
LOF	0	0	1	0
GOF	0	0	0	1

Sum of each orthogonal contrast should be zero: chargeampa

	sum
WT_vs_Mutants	0
LOF_vs_GOF	0
LOF	0
GOF	0

Matrix of contrasts on mutation: chargeampa

mutation	WT_vs_Mutants	LOF_vs_GOF	LOF	GOF
WT	-0.8	0.0	0.0	0.0
R540H	0.2	0.5	0.0	-0.5
R696H	0.2	0.5	0.0	0.5
C456Y	0.2	-0.5	-0.5	0.0
C461F	0.2	-0.5	0.5	0.0

Matrix of contrasts on transfection: chargeampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargeampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	5.96	4	15.69	.004	11
transfection	1.66	1	91.00	.200	0.237
mutation:transfection	1.88	4	91.00	.121	0.504
mutationWT_vs_Mutants:transfection1	1.87	1	91.00	.174
mutationLOF_vs_GOF:transfection1	2.82	1	91.00	.097
mutationLOF:transfection1	3.42	1	91.00	.068
mutationGOF:transfection1	0.06	1	91.00	.803

Intraclass correlation coefficients for random effects: chargeampa

Group	ICC	N
pair:(slice:animal)	0.172	96
slice:animal	0.149	62
animal	0.191	22
residual	0.488	192

Estimated marginal means with 95% confidence intervals: chargeampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	19.28	0.29	19	0.18	0.48
R540H	-	19.74	0.82	27	0.54	1.26
R696H	-	22.00	1.27	17	0.76	2.11
C456Y	-	25.67	1.02	18	0.64	1.64
C461F	-	18.70	0.79	15	0.45	1.39
WT	+	19.28	0.33	19	0.20	0.54
R540H	+	19.74	0.84	27	0.55	1.29
R696H	+	22.00	1.22	17	0.74	2.03
C456Y	+	25.67	0.97	18	0.60	1.55
C461F	+	18.70	0.46	15	0.26	0.81

[1] “Overall average of chargeampa for untransfected neurons: 0.76”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargeampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	91	1.12	0.80	1.57
(+) / (-)	R540H	91	1.02	0.77	1.36
(+) / (-)	R696H	91	0.97	0.67	1.38
(+) / (-)	C456Y	91	0.94	0.67	1.34
(+) / (-)	C461F	91	0.58	0.40	0.85

95% confidence intervals for contrasts: chargeampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
R540H / WT	(+) / (-)	91	0.92	0.59	1.43
R696H / WT	(+) / (-)	91	0.86	0.53	1.42
C456Y / WT	(+) / (-)	91	0.85	0.52	1.37
C461F / WT	(+) / (-)	91	0.52	0.31	0.87

Standardized effect sizes (r) for contrasts: chargeampa

mutation	transfection	n	r	95% CI
R540H / WT	(+) / (-)	96	-0.04	[-0.24, 0.16]
R696H / WT	(+) / (-)	96	-0.06	[-0.26, 0.14]
C456Y / WT	(+) / (-)	96	-0.07	[-0.27, 0.13]
C461F / WT	(+) / (-)	96	-0.26	[-0.43, -0.06]

## Note: df set to 91

Hypothesis testing on interaction parameters (Dunnett’s step-down p-value adjustment): chargeampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	ratio	df	null	t.ratio	p.value	p.adj	maxBF
R540H / WT	(+) / (-)	0.92	91	1	-0.39	.695	.831	1
R696H / WT	(+) / (-)	0.86	91	1	-0.58	.560	.831	1
C456Y / WT	(+) / (-)	0.85	91	1	-0.68	.495	.831	1
C461F / WT	(+) / (-)	0.52	91	1	-2.52	.013	.046	2.6